Published
Edited
Sep 22, 2022
1 star
Ex10
iris = FileAttachment("iris.csv").csv()
import { SummaryTable } from "@observablehq/summary-table"
viewof summary_data = SummaryTable(iris, {label: "Iris Data"})
irisTyped = iris.map(d => {
return {
sepal_length: +d.sepal_length,
sepal_width: +d.sepal_width,
petal_length: +d.petal_length,
petal_width: +d.petal_width,
species: d.species
}
});
viewof summary__iris_typed_data = SummaryTable(irisTyped, {label: "Iris Typed Data"})
import {vl} from '@vega/vega-lite-api-v5'
vl.markPoint().data(iris).encode(
vl.x().fieldQ('sepal_length').title('Sepal length (cm)'),
vl.y().fieldQ('sepal_width').title('Sepal width (cm)')
).title("Iris Dataset").render()
vl.markPoint().data(iris).encode(
vl.x().fieldQ('sepal_length').title('Sepal length (cm)'),
vl.y().fieldQ('sepal_width').title('Sepal width (cm)'),
vl.color().fieldN('species'),
vl.size().fieldQ('petal_length')
// vl.tooltip(['Name', 'Origin']) // show the Name and Origin fields in a tooltip
).title("Iris Dataset").render()
vega_embed = require("vega-embed@5")
plot = {
yield {
"$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json",
"title": "Iris Dataset",
"data": {"values":iris},
"config": {"view": {"width": 400, "height": 300}},
"mark": "point",
"encoding": {
"color": {"type": "nominal", "field": "species"},
"size":{"type": "quantitative", "field": "petal_length"},
"x": {"type": "quantitative", "field": "sepal_length"},
"y": {"type": "quantitative", "field": "sepal_width"}
}
}
}
vega_embed(plot)
SMO = FileAttachment("SMO-VOR-2015.csv").csv()
viewof summary_data_SMO = SummaryTable(SMO, {label: "SMO VOR 2015 dataset"})
vl.markPoint().data(SMO).encode(
vl.x().fieldT('ptime').title('Penetration time'),
vl.y().fieldT('alt').title('Altitude (feets)'),
vl.tooltip(["flight"])
).title("SMO VOR 2015 dataset").render()
vl.markBoxplot().data(SMO).encode(
vl.x().fieldO('month').title(null),
vl.y().fieldT('alt').title('Altitude (feets)'),
vl.color().fieldN('ops'),
vl.tooltip("flight")
).title("SMO VOR 2015 dataset").render()
vl.markBar().data(SMO).encode(
vl.x().fieldT('alt').title('Altitude (feets)'),
vl.y().count().title("Counts")
).title("SMO VOR 2015 dataset").render()
vl.markBar().data(SMO).encode(
vl.x().fieldT('alt').title('Altitude (feets)'),
vl.y().count().title("Counts"),
vl.column().fieldT('ptime').timeUnit('utcmonth')
).title("SMO VOR 2015 dataset").render()
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Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
